scholarly journals A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 263
Author(s):  
Amal Altamimi ◽  
Belgacem Ben Ben Youssef
Keyword(s):  
Remote Sensing ◽  
Systematic Review ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Sensor Technology ◽  
Future Research ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
The Impact

Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. We reviewed a total of 101 papers published from 2000 to 2021. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research.

scholarly journals Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications

2021 ◽  
Vol 11 (11) ◽  
pp. 4878
Author(s):  
Ivan Racetin ◽  
Andrija Krtalić
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Anomaly Detection ◽  
Target Detection ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Detection Methods ◽  
Hyperspectral Image Processing ◽  
Sensing Applications ◽  
Remote Sensing Applications

Hyperspectral sensors are passive instruments that record reflected electromagnetic radiation in tens or hundreds of narrow and consecutive spectral bands. In the last two decades, the availability of hyperspectral data has sharply increased, propelling the development of a plethora of hyperspectral classification and target detection algorithms. Anomaly detection methods in hyperspectral images refer to a class of target detection methods that do not require any a-priori knowledge about a hyperspectral scene or target spectrum. They are unsupervised learning techniques that automatically discover rare features on hyperspectral images. This review paper is organized into two parts: part A provides a bibliographic analysis of hyperspectral image processing for anomaly detection in remote sensing applications. Development of the subject field is discussed, and key authors and journals are highlighted. In part B an overview of the topic is presented, starting from the mathematical framework for anomaly detection. The anomaly detection methods were generally categorized as techniques that implement structured or unstructured background models and then organized into appropriate sub-categories. Specific anomaly detection methods are presented with corresponding detection statistics, and their properties are discussed. This paper represents the first review regarding hyperspectral image processing for anomaly detection in remote sensing applications.

scholarly journals PIECEWISE ANOMALY DETECTION USING MINIMAL LEARNING MACHINE FOR HYPERSPECTRAL IMAGES

2021 ◽  
Vol V-3-2021 ◽  
pp. 89-96
Author(s):  
A-M. Raita-Hakola ◽  
I. Pölönen
Keyword(s):  
Remote Sensing ◽  
Anomaly Detection ◽  
Real Time ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Computationally Efficient ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
Learning Machine ◽  
Minimal Learning Machine

Abstract. Hyperspectral imaging, with its applications, offers promising tools for remote sensing and Earth observation. Recent development has increased the quality of the sensors. At the same time, the prices of the sensors are lowering. Anomaly detection is one of the popular remote sensing applications, which benefits from real-time solutions. A real-time solution has its limitations, for example, due to a large amount of hyperspectral data, platform’s (drones or a cube satellite) constraints on payload and processing capability. Other examples are the limitations of available energy and the complexity of the machine learning models. When anomalies are detected in real-time from the hyperspectral images, one crucial factor is to utilise a computationally efficient method. The Minimal Learning Machine is a distance-based classification algorithm, which can be modified for anomaly detection. Earlier studies confirms that the Minimal learning Machine (MLM) is capable of detecting efficiently global anomalies from the hyperspectral images with a false alarm rate of zero. In this study, we will show that by using a carefully selected lower threshold besides the higher threshold of the variance, it is possible to detect local and global anomalies with the MLM. The downside is that the improved method is highly sensitive with the respect to the noise. Thus, the second aim of this study is to improve the MLM’s robustness with respect to noise by introducing a novel approach, the piecewise MLM. With the new approach, the piecewise MLM can detect global and local anomalies, and the method is significantly more robust with respect to noise than the MLM. As a result, we have an interesting, easy to implement and computationally light method which is suitable for remote sensing applications.

scholarly journals Remote Sensing by using Unsupervised Algorithm

2021 ◽  
Author(s):  
Afshan Saleem
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Unsupervised Learning ◽  
Hyperspectral Image ◽  
Relevant Information ◽  
Spectral Signature ◽  
Sensing Applications ◽  
Hyper Spectral ◽  
Wide Range ◽  
Remote Sensing Applications

Hyper-spectral images contain a wide range of bands or wavelength due to which they are rich in information. These images are taken by specialized sensors and then investigated through various supervised or unsupervised learning algorithms. Data that is acquired by hyperspectral image contain plenty of information hence it can be used in applications where materials can be analyzed keenly, even the smallest difference can be detected on the basis of spectral signature i.e. remote sensing applications. In order to retrieve information about the concerned area, the image has to be grouped in different segments and can be analyzed conveniently. In this way, only concerned portions of the image can be studied that have relevant information and the rest that do not have any information can be discarded. Image segmentation can be done to assort all pixels in groups. Many methods can be used for this purpose but in this paper, we discussed k means clustering to assort data in AVIRIS cuprite, AVIRIS Muffet and Rosis Pavia in order to calculate the number of regions in each image and retrieved information of 1st, 10th and100th band. Clustering has been done easily and efficiently as k means algorithm is the easiest approach to retrieve information.

scholarly journals HYPERSPECTRAL IMAGE CLASSIFICATION BY EXPLOITING CONVOLUTIONAL NEURAL NETWORKS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 535-540 ◽  
Author(s):  
B. Hosseiny ◽  
H. Rastiveis ◽  
S. Daneshtalab
Keyword(s):  
Hyperspectral Image ◽  
Training Sample ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Training Sample Size ◽  
Sensing Applications ◽  
Processing Step ◽  
Remote Sensing Applications ◽  
Spectral Dimensionality ◽  
Learning Concept

Abstract. High spectral dimensionality of hyperspectral images makes them useful data resources for earth observation in many remote sensing applications. In this case, the convolutional neural network (CNN) can help to extract deep and robust features from hyperspectral images. The main goal of this paper is to use deep learning concept to extract deep features from hyperspectral datasets to achieve better classification results. In this study, after pre-processing step, data is fed to a CNN in order to extract deep features. Extracted features are then imported in a multi-layer perceptron (MLP) network as our selected classifier. Obtained classification accuracies, based on training sample size, vary from 94.3 to 97.17% and 92.35 to 98.14% for Salinas and Pavia datasets, respectively. These results expressed more than 10% improvements compared to the classic MLP classification technique.

Efficient dimension reduction of hyperspectral images for big data remote sensing applications

2020 ◽  
Vol 14 (03) ◽  
pp. 1
Author(s):  
Beatriz P. Garcia-Salgado ◽  
Volodymyr I. Ponomaryov ◽  
Sergiy Sadovnychiy ◽  
Rogelio Reyes-Reyes
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Dimension Reduction ◽  
Hyperspectral Images ◽  
Sensing Applications ◽  
Remote Sensing Applications

On the parallel classification system using hyperspectral images for remote sensing applications

2018 ◽  
Author(s):  
Volodymyr I. Ponomaryov ◽  
Beatriz P. Garcia-Salgado ◽  
Cesar M. A. Robles-Gonzalez ◽  
Sergiy Sadovnychiy
Keyword(s):  
Remote Sensing ◽  
Classification System ◽  
Hyperspectral Images ◽  
Sensing Applications ◽  
Remote Sensing Applications

scholarly journals Physically Based Approach for Combined Atmospheric and Topographic Corrections

2019 ◽  
Vol 11 (10) ◽  
pp. 1218 ◽  
Author(s):  
Federico Santini ◽  
Angelo Palombo
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Test Cases ◽  
Remote Sensors ◽  
Sensing Applications ◽  
Physically Based ◽  
Remote Sensing Applications ◽  
The Impact

The enhanced spectral and spatial resolutions of the remote sensors have increased the need for highly performing preprocessing procedures. In this paper, a comprehensive approach, which simultaneously performs atmospheric and topographic corrections and includes second order corrections such as adjacency effects, was presented. The method, developed under the assumption of Lambertian surfaces, is physically based and uses MODTRAN 4 radiative transfer model. The use of MODTRAN 4 for the estimates of the radiative quantities was widely discussed in the paper and the impact on remote sensing applications was shown through a series of test cases.

scholarly journals Quantifying the Impact of Solar Spectra on the Inter-Calibration of Satellite Instruments

2021 ◽  
Vol 13 (8) ◽  
pp. 1438
Author(s):  
Rajendra Bhatt ◽  
David R. Doelling ◽  
Odele Coddington ◽  
Benjamin Scarino ◽  
Arun Gopalan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Reference Spectrum ◽  
Absolute Accuracy ◽  
Common Reference ◽  
The Past ◽  
Sensing Applications ◽  
Satellite Retrievals ◽  
Remote Sensing Applications ◽  
Spectral Solar Irradiance ◽  
The Impact

In satellite-based remote sensing applications, the conversion of the sensor recorded top-of-atmosphere reflectance to radiance, or vice-versa, is carried out using a reference spectral solar irradiance (SSI) dataset. The choice of reference SSI spectrum has consistently changed over the past four decades with the increasing availability of more accurate SSI measurements with greater spectral coverage. Considerable differences (up to 15% at certain wavelengths) exist between the numerous SSI spectra that are currently being used in satellite ground processing systems. The aim of this study is to quantify the absolute differences between the most commonly used SSI datasets and investigate their impact in satellite inter-calibration and environmental retrievals. It was noted that if analogous SNPP and NOAA-20 VIIRS channel reflectances were perfectly inter-calibrated, the derived channel radiances can still differ by up to 3% due to the utilization of differing SSI datasets by the two VIIRS instruments. This paper also highlights a TSIS-1 SIM-based Hybrid Solar Reference Spectrum (HSRS) with an unprecedented absolute accuracy of 0.3% between 460 and 2365 nm, and recommends that the remote sensing community use it as a common reference SSI in satellite retrievals.

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